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Selecting good views of high‐dimensional data using class consistency
Author(s) -
Sips Mike,
Neubert Boris,
Lewis John P.,
Hanrahan Pat
Publication year - 2009
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/j.1467-8659.2009.01467.x
Subject(s) - consistency (knowledge bases) , class (philosophy) , computer science , constraint (computer aided design) , range (aeronautics) , consistency model , visualization , data mining , weak consistency , measure (data warehouse) , theoretical computer science , data consistency , information retrieval , mathematics , artificial intelligence , strong consistency , statistics , database , materials science , geometry , estimator , composite material
Many visualization techniques involve mapping high‐dimensional data spaces to lower‐dimensional views. Unfortunately, mapping a high‐dimensional data space into a scatterplot involves a loss of information; or, even worse, it can give a misleading picture of valuable structure in higher dimensions. In this paper, we propose class consistency as a measure of the quality of the mapping. Class consistency enforces the constraint that classes of n–D data are shown clearly in 2–D scatterplots. We propose two quantitative measures of class consistency, one based on the distance to the class's center of gravity, and another based on the entropies of the spatial distributions of classes. We performed an experiment where users choose good views, and show that class consistency has good precision and recall. We also evaluate both consistency measures over a range of data sets and show that these measures are efficient and robust.